Reducing error in data compression

ABSTRACT

Systems and methods are provided for reducing error in data compression and decompression when data is transmitted over low bandwidth communication links, such as satellite links. Embodiments of the present disclosure provide systems and methods for variable block size compression for gridded data, efficiently storing null values in gridded data, and eliminating growth of error in compressed time series data.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation application of U.S. patentapplication Ser. No. 17/039,720, filed on Sep. 30, 2020, which is adivisional application of U.S. patent application Ser. No. 16/378,766,filed on Apr. 9, 2019 (now U.S. Pat. No. 11,025,273), which claims thebenefit of U.S. Provisional Patent Application No. 62/654,741, filed onApr. 9, 2018, all of which are incorporated by reference herein in theirentireties.

FIELD OF THE DISCLOSURE

This disclosure relates to data compression techniques, includingreducing error in data compression techniques.

BACKGROUND

Many data sets include large amounts of data that is compressed beforeit is processed to make computation easier. Further, some data sets aretransmitted over low bandwidth links, such as satellites, that requiredata compression for transmission. Systems and methods are needed tocompress large data sets in an efficient way while minimizing error indata compression and/or decompression.

BRIEF DESCRIPTION OF THE DRAWINGS/FIGURES

The accompanying drawings, which are incorporated in and constitute partof the specification, illustrate embodiments of the disclosure and,together with the general description given above and the detaileddescriptions of embodiments given below, serve to explain the principlesof the present disclosure. In the drawings:

FIG. 1 is a diagram of an exemplary system that compresses anddecompresses data in accordance with an embodiment of the presentdisclosure;

FIG. 2 is a diagram of another exemplary system that compresses anddecompresses data in accordance with an embodiment of the presentdisclosure;

FIG. 3 is an exemplary diagram representing surface temperature of thePacific Ocean off the west coast of North America in accordance with anembodiment of the present disclosure;

FIG. 4 shows results of a Discrete Cosine Transform (DCT) in accordancewith an embodiment of the present disclosure;

FIG. 5 shows matrices illustrating an exemplary quantization step inaccordance with an embodiment of the present disclosure;

FIG. 6 shows diagrams illustrating an exemplary variable compressionmethod in accordance with an embodiment of the present disclosure;

FIG. 7 is an image of an area of ocean temperature data in accordancewith an embodiment of the present disclosure;

FIG. 8 is a flowchart of an exemplary method for variable block sizecompression in accordance with an embodiment of the present disclosure:

FIG. 9 is a diagram illustrating how a 2 dimensional (2D) grid of datacan be zig-zag encoded to create a linear signal in accordance with anembodiment of the present disclosure;

FIG. 10 is a flowchart of an exemplary method for efficiently storingnull value in gridded data in accordance with an embodiment of thepresent disclosure;

FIG. 11A shows diagrams illustrating the creation of an exemplary timeseries difference grid in accordance with an embodiment of the presentdisclosure;

FIG. 11B shows diagrams illustrating the creation of an exemplary timeseries difference grid accounting for compression error in accordancewith an embodiment of the present disclosure;

FIG. 12 is a flowchart for a method for eliminating growth of error incompressed time series data in accordance with an embodiment of thepresent disclosure; and

FIG. 13 is a flowchart of a method for determining how to reducing errorin data compression in accordance with an embodiment of the presentdisclosure.

Features and advantages of the present disclosure will become moreapparent from the detailed description set forth below when taken inconjunction with the drawings, in which like reference charactersidentify corresponding elements throughout. In the drawings, likereference numbers generally indicate identical, functionally similar,and/or structurally similar elements. The drawing in which an elementfirst appears is indicated by the leftmost digit(s) in the correspondingreference number.

DETAILED DESCRIPTION

In the following description, numerous specific details are set forth toprovide a thorough understanding of the disclosure. However, it will beapparent to those skilled in the art that the disclosure, includingstructures, systems, and methods, may be practiced without thesespecific details. The description and representation herein are thecommon means used by those experienced or skilled in the art to mosteffectively convey the substance of their work to others skilled in theart. In other instances, well-known methods, procedures, components, andcircuitry have not been described in detail to avoid unnecessarilyobscuring aspects of the disclosure.

References in the specification to “one embodiment,” “an embodiment,”“an exemplary embodiment,” etc., indicate that the embodiment describedmay include a particular feature, structure, or characteristic, butevery embodiment may not necessarily include the particular feature,structure, or characteristic. Moreover, such phrases are not necessarilyreferring to the same embodiment. Further, when a particular feature,structure, or characteristic is described in connection with anembodiment, it is submitted that it is within the knowledge of oneskilled in the art to understand that such description(s) can affectsuch feature, structure, or characteristic in connection with otherembodiments whether or not explicitly described.

1. Overview

Embodiments of the present disclosure provide systems and methods forreducing error in data compression and decompression when data istransmitted over low bandwidth communication links, such as satellitelinks. Embodiments of the present disclosure provide systems and methodsfor variable block size compression for gridded data by subdividingblocks if error for a decompressed block is above a predeterminedthreshold. Further, embodiments of the present disclosure providesystems and methods for efficiently storing null values in gridded data.Embodiments of the present disclosure also provide systems and methodsfor allowing time based series of gridded scientific data to becompressed more efficiently.

2. Data Compression and Decompression for Low Bandwidth Communication

FIG. 1 is a diagram of an exemplary system that compresses anddecompresses data in accordance with an embodiment of the presentdisclosure. FIG. 1 includes a computing device 102 that communicateswith three end user devices 114 via a data router 112. In FIG. 1 ,computing device 102 includes a data compressor 104, processor 106, andmemory 108, and end user devices 114 include respective data compressors116, processors 118, and memories 120.

The communication links between computing device 102 and data router 112and between data router 112 and each of end user devices 114 can bewired or wireless communication links and long range or short rangecommunication links in accordance with embodiments of the presentdisclosure. In an embodiment, the communication link between computingdevice 102 and data router 112 and between data router 112 and each ofend user devices 114 can be low bandwidth communication links. Forexample, in an embodiment, data router 112 is a satellite and has lowbandwidth upload and download communication links to and from computingdevice 102 and end user devices 114.

In an embodiment, computing device 102 is configured to compress data(e.g., using data compressor 104) prior to sending data to data router112 (e.g., due to the low bandwidth link between computing device 102and data router 112). In an embodiment, the data sent from computingdevice 102 can include destination information instructing data router112 where to route the information (e.g., specifying sufficient routinginformation for data router 112 to route the information to one of enduser devices 114). In an embodiment, data router 112 is configured tosend data (e.g., based on the routing information) to a specified enduser device (e.g., end user device 114 a). In an embodiment, thereceiving end user device (e.g., end user device 114 a) is configured todecompress the received data (e.g., using data compressor 116 a) so thatthe decompressed data can be more easily processed.

Data compressor 104 of computing device 104 and data compressors 116 ofend user devices 114 can be configured to perform data compression, datadecompression, or data compression and data decompression in accordancewith embodiments of the present disclosure. For example, in anembodiment, end user device 114 b can use data compressor 116 b tocompress data and send it to computing device 102 (via data router 112).In an embodiment, computing device 102 can decompress the received data(e.g., using data compressor 104).

FIG. 2 is a diagram of another exemplary system that compresses anddecompresses data in accordance with an embodiment of the presentdisclosure. In FIG. 2 , data gathering devices 202 gather data (e.g.,climatological data) and send the gathered data, via a satellite 212, toa computing center 214 to be processed (e.g., by a supercomputer 216that includes a data compressor 218, a processor 220, and a memory 222).Data gathering devices 202 can include a variety of devices, including,but not limited to, satellites(s) 204 (e.g., sensing climatological datafrom space), buoy(s) 206 (e.g., measuring currents and/or temperaturesof water), and ship(s) 208 (e.g., via onboard sensors for measuringcurrents and/or temperatures of water).

In an embodiment, one or more of data gathering devices 202 (e.g.,satellite(s) 204, buoy(s) 206, and/or ship(s) 208) include datacompressors 210. In an embodiment, data gathering devices 202 do notinclude data compressors 210. In an embodiment, the communication linkbetween data gathering devices 202 and satellite 212 and/or thecommunication link between satellite 212 and computing center 214 arelow bandwidth communication links, and data from one or more of datagathering devices 202 is compressed (e.g., using data compressors 210)before it is transmitted to satellite 212. In an embodiment, datagathering devices 202 transmit uncompressed data to satellite 212. Datatransmitted from data gathering devices 202 can include destinationinformation instructing satellite 212 where to transmit data (e.g.,identifying location information for computing center 214 and/orsupercomputer 216).

In an embodiment, supercomputer 216 of computing center 214 receives thedata from satellite 212 and processes the data. In an embodiment,supercomputer 216 gathers and processes a large amount of data thatcannot be easily transmitted to end user devices (e.g., ships 226) andcompresses the data for easier transmission (e.g., using data compressor218). For example, in an embodiment, supercomputer 216 gathersclimatological data from a variety of sources (e.g., from satellite(s)204, buoy(s) 206, and/or ship(s) 208) and generates a forecast. In anembodiment, supercomputer 216 compresses the forecast data, using datacompressor 218, and sends it to one or more of ships 226 via satellite224. In an embodiment, the communication link between computing center214 and satellite 224 and the communication link between satellite 224and ships 226 are low bandwidth communication links. In an embodiment,each of ships 226 includes a data compressor 228, a processor 230, and amemory 232.

Data compressors 104, 116, 210, 218, and 228 can be implemented usinghardware, software, and/or a combination of hardware and software inaccordance with embodiments of the present disclosure. Further, datacompressors 104, 116, 210, 218, and 228 can be implemented using asingle device (e.g., a single chip) or multiple devices. Datacompressors 104, 116, 210, 218, and 228 can be integrated into a hostdevice (e.g., integrated into computing device 102, end user device 114,satellite 204, buoy 206, ship 208, supercomputer 216, and/or ships 226).

In an embodiment, data compressors 104, 116, 210, 218, and 228 can beimplemented as standalone (e.g., special purpose) devices. In anembodiment, data compressors 104, 116, 210, 218, and 228 can include oneor more processors and/or memories. In an embodiment, computing device102 can be a data compressor device, and data compressor 104 can behardware or software (stored within memory 108 or outside of memory 108)for performing data compression and/or decompression functions. In anembodiment data router 112, satellite 212, and/or satellite 224 can alsoinclude data compressors, such as data compressors 104, 116, 210, 218,and 228.

In an embodiment, data compressors 228 can be configured to compress,decompress, or compress and decompress data. For example, in anembodiment, supercomputer 216 transmits compressed forecast data to ship226 a to satellite 224 and includes destination information for ship 226a. In an embodiment, satellite 224 relays the data to ship 226 a, andship 226 a decompresses the compressed forecast data using datacompressor 228 a so that the decompressed forecast data can be used byship 226 a.

While data compressors in accordance with embodiments of the presentdisclosure have been described above with reference to embodiments withcomputing devices (e.g., computing device 102) and end user devices(e.g., end user devices 114) supercomputers (e.g., supercomputer 216),and ships (e.g., ships 226), it should be understood that datacompressors in accordance with embodiments of the present disclosure canbe used in a wide variety of systems, methods, and devices.

Embodiments of the present disclosure provide systems and methods toenable data compressors 104, 116, 210, 218, and 228 to compress and/ordecompress data more effectively (e.g., more efficiently and with fewererrors due to data compression and/or decompression). For example,embodiments of the present disclosure provide systems and methods toenable data compressors 104, 116, 210, 218, and 228 to use variableblock size compression for gridded data by subdividing blocks if errorfor a decompressed block is above a predetermined threshold. Further,embodiments of the present disclosure provide systems and methods toenable data compressors 104, 116, 210, 218, and 228 to efficiently storenull values in gridded data. Embodiments of the present disclosure alsoprovide systems and methods to enable data compressors 104, 116, 210,218, and 228 to allow time based series of gridded scientific data to becompressed more efficiently. These systems and methods to enable datacompressors 104, 116, 210, 218, and 228 to compress and/or decompressdata more effectively will now be discussed in more detail.

3. Variable Block Size Compression for Gridded Data

Embodiments of the present disclosure provide systems and methods forallowing data (e.g., gridded data from scientific observations) to becompressed using a variable block size technique. FIG. 3 is an exemplarydiagram representing surface temperature of the Pacific Ocean off thewest coast of North America in accordance with an embodiment of thepresent disclosure. The white areas in FIG. 3 represent land (the westcoast of the North American continent), and the shaded colors representwater temperature values. In an embodiment, water temperature data fromFIG. 3 can be stored (e.g., using a grid having rows and columns ofdata). In an embodiment, all grid values are stored as 32 bit floatingpoint values. Because the data set of ocean water temperatures can bevery large, the data can be compressed for easier transmission and/orprocessing.

In an embodiment, compression algorithms use a grid oriented approach.For example, instead of processing an entire grid of data at once,grid-oriented algorithms can compress smaller blocks of data in the grid(e.g., block sizes of 8×8, 16×16, 32×32, 64×64, etc.). In an embodiment,grids and/or blocks of data can be represented as matrices and can beprocessed using Discrete Cosine Transform (DCT) or similar techniquesfor data compression purposes. Larger block sizes can require morecomputational effort but can allow for more compression.

3.1. Matrix Compression

FIG. 4 shows results of a Discrete Cosine Transform (DCT) in accordancewith an embodiment of the present disclosure. The picture matrix 404represents the original data 402. The DCT matrix 408 represents DCT data406 after the DCT has been applied to the original data 402. In anembodiment, DCT matrix 408 can be highly compressed. As shown in FIG. 4, much of the data in DCT matrix 408 is packed into the upper leftcorner of DCT matrix 408. In an embodiment, to compress data in DCTmatrix 408, after creation of DCT matrix 408, a quantization matrix(also called a threshold matrix) is applied that reduces many of thesmall values in DCT matrix 408 to zero.

FIG. 5 shows matrices illustrating an exemplary quantization step inaccordance with an embodiment of the present disclosure. In FIG. 5 , aquantization matrix 504 is applied to a DCT matrix 502 (e.g., containingDCT coefficients). For example, in an embodiment, DCT matrix 502 isdivided (e.g., element by element) by quantization matrix 504, and,after the results are rounded to integers, zeroed matrix 506 isproduced. As shown in FIG. 5 , zeroed matrix 506 is mostly zeroes and istherefore highly compressed. In an embodiment, DCT matrix 502 is fullyinvertible, but zeroed matrix 506 is not invertible. Thus, compressingDCT matrix 502 into zeroed matrix 506 can make DCT matrix 502 easier towork with but can also be a lossy process. In an embodiment, smallerblock sizes require less computation for the DCT step, but theytypically provide lower compression. In practice, compression algorithmstry to pick a block size that balances the computational time needed,the compression desired, and the tolerated loss.

3.2. Variable Block Size Compression

Embodiments of the present disclosure provide systems and methods thatenable the block size of a compression algorithm to be dynamicallychanged. In an embodiment, dynamically changing the block size of acompression algorithm enables compression of data to be optimized whilemaintaining a tolerable amount of loss.

FIG. 6 shows diagrams illustrating an exemplary variable compressionmethod in accordance with an embodiment of the present disclosure. In anembodiment, the exemplary variable compression method starts with anoriginal block 602 of input data to be compressed (e.g., a 32×32matrix). In an embodiment, the entire grid of data can be used asoriginal block 602. In an embodiment, if the grid of input data isespecially large, the grid of input data can first be subdivided into amaximum block size (e.g., based on a predetermined maximum block sizethreshold, such as a 32×32 block) before the blocks are compressed. Inan embodiment, each block is compressed (e.g., using a transform andquantization technique as described above with reference to FIG. 5 ).For example, in FIG. 6 , original block 602 is compressed.

In an embodiment, the compressed blocks are then decompressed andcompared to the original data. The decompressed version will likely besomewhat different from the original data. In an embodiment, if thedifferences between the decompressed block and the original data aregreater than a predetermined threshold, then the larger original blockis sub-divided into sub-blocks 604 (e.g., into 4 equally sizedsub-blocks). In an embodiment, the number of sub-blocks to create isbased on a predetermined threshold (e.g., 4 sub-blocks). In FIG. 6 , the32×32 original block 602 is divided into 4 16×16 sub-blocks 604. Each ofsub-blocks 604 can then be compressed, and the process can be repeated.For example, in FIG. 6 , sub-block 606 was shown to be outside of theerror tolerance range and was subdivided into sub-blocks 608, which wereall shown to be within the error tolerance range.

In an embodiment, the process is recursive, and as long as a block'scompressed version is outside of the error tolerance, the block cancontinue to be sub-divided. In an embodiment, blocks outside of theerror tolerance range can continue to be subdivided down to a block sizeof 1×1. In an embodiment, in the case of a 1×1 block, the DCT result isthe same as the original data, and the threshold comparison will showalmost no loss.

FIG. 7 is an image of an area of ocean temperature data in accordancewith an embodiment of the present disclosure. In FIG. 7 , the top leftpart of the image is a warmer region than the bottom right. Grouping allthese grid cells into the same compression block will cause both regionsto be shifted towards each other's temperatures. Using a dynamiccompression method in accordance with an embodiment of the presentdisclosure allows the different regions of the image of FIG. 7 to becompressed separately and more accurately.

3.3. Exemplary Systems and Methods Using Variable Block Size Compression

FIG. 8 is a flowchart of an exemplary method for variable block sizecompression in accordance with an embodiment of the present disclosure.In step 802, data to be compressed is received. For example, in anembodiment, a grid of data of surface ocean temperature (e.g., as shownin FIG. 3 ) is received. For example, in an embodiment, satellite 204collects the ocean surface temperature data and transmits the data tocomputing center 214, where it is received by supercomputer 216.

In step 804, the received data is divided into original blocks, ifnecessary. For example, in an embodiment, supercomputer 216 (e.g., usingprocessor 220 and/or data compressor 218) determines whether tosubdivide the received data into blocks of a predetermined maximum blocksize before further processing. In step 806, each block of the receiveddata is compressed (e.g., using compressor 218). For example, in anembodiment, supercomputer 216 (e.g., using processor 220 and/or datacompressor 218) can perform a DCT on each block of original data tocompress the data, thereby generating DCT matrices for each block oforiginal data. In an embodiment, a quantization matrix can be applied toeach DCT matrix, and the resulting values in the matrices can be roundedto integers, thereby generating a plurality of zeroed matrices ascompressed data.

Steps 808, 810, and 812 are then performed on each block of compresseddata. In step 808, a block of compressed data is decompressed (e.g.,using compressor 218 and/or processor 220) and compared to theuncompressed block of data. In step 810, a determination is made (e.g.,using compressor 218 and/or processor 220) regarding whether thedifferences between the decompressed block of data and the uncompressedblock of data exceed a predetermined threshold (e.g., a predeterminederror tolerance threshold). In step 812, if the differences between thedecompressed block of data and the uncompressed block of data exceed thepredetermined threshold, the block of input data is subdivided (e.g.,using compressor 218 and/or processor 220).

For example, in an embodiment, if the differences between thedecompressed block of data and the uncompressed block of data exceed thepredetermined threshold, a determination can be made (e.g., usingcompressor 218 and/or processor 220) that a compression error hasoccurred for the block of data. In an embodiment, if the differencesbetween the decompressed block of data and the uncompressed block ofdata do not exceed the predetermined threshold, a determination can bemade (e.g., using compressor 218 and/or processor 220) that nocompression error has occurred for the block of data.

In an embodiment, steps 808, 810, and 812 are performed on each block ofcompressed data until each block of compressed data has been determinedto either not exceed the predetermined error tolerance threshold or hasbeen further subdivided in response to a determination that the datacompression for the block has resulted in the error tolerance thresholdhas been exceeded. Steps 808, 810, and 812 can be performed in series orin parallel in accordance with embodiments of the present disclosure.For example, in an embodiment, each block of compressed data can bechecked against the error tolerance threshold (e.g., using steps 808,810, and 812) in turn, or all blocks of data can be checked against theerror tolerance threshold at the same time.

In FIG. 8 , the method then returns to step 806 and is repeated on eachblock of newly subdivided data. For example, in an embodiment, if ablock of data was determined to exceed the error tolerance threshold instep 810, the block of data is subdivided in step 812 (e.g., into 4 newblocks). In step 806, these 4 new blocks can be compressed, and each ofthese 4 new blocks can be checked against the error tolerance thresholdusing steps 808, 810, and 812. In an embodiment, if multiple blocks ofdata were determined to exceed the error tolerance threshold in step810, each block of data that exceeded the error tolerance threshold issubdivided in step 812 (e.g., into 4 new blocks). In step 806, each setof subdivided blocks can be compressed, and each of these new subdividesets of blocks can be checked against the error tolerance thresholdusing steps 808, 810, and 812. Operations on each set of newlysubdivided blocks that exceeded the error tolerance threshold can beperformed in series or in parallel in accordance with embodiments of thepresent disclosure.

3.4. Exemplary Advantages and Alternatives of Using Variable Block SizeCompression

Embodiments of the present disclosure using variable block sizecompression data grids that exhibit high variability to be easily andefficiently compressed. Embodiments of the present disclosure usingvariable block size compression enable a compression method to be tunedto an exact error tolerance. This is a key requirement for scientificdata. For example, a temperature grid can be compressed with error lessthan 0.01 degrees. This enables optimal compression controlled fortolerated loss.

Embodiments of the present disclosure using variable block sizecompression use the real error of the compression step and thusdefinitively control the error. In an embodiment, storing markers thatindicate when a block has been subdivided can be used. For example, inan embodiment, 16 bit (short) numbers are used to store data values. Inan embodiment, the last value in this range can be reserved to indicatethat a block has been subdivided. This results in negligible loss ofprecision and doesn't require addition of a header or marker byte to thedata sequence.

4. Efficiently Storing Null Values in Gridded Data

As described above, FIG. 3 is an exemplary diagram representing surfacetemperature of the Pacific Ocean off the west coast of North America. InFIG. 3 , the white areas representing land can be considered null areasbecause they are not relevant for ocean temperature values. Embodimentsof the present disclosure provide systems and methods for efficientlystoring null values to improve data compression.

4.1. Storing Null Values Using Sentinel Values

One method for representing null areas in a data grid is to representdata at these null areas with a marker or sentinel value that indicatesthe lack of data (e.g., ocean temperature data) in the data grid cellfor these locations. Typical marker values can be selected such thatthey are not likely to be misinterpreted as real data. Examples wouldinclude −999999, 99999, 888888, or similar values.

While using sentinel values makes it simple to store and interpret gridswith null area values, using sentinel values can cause problems for datacompression algorithms. Compression algorithms reduce the amount ofstorage space needed for the data. Lossless compression reduces bits byidentifying and eliminating statistical redundancy. No information islost in lossless compression. Lossy compression reduces bits by removingunnecessary or less important information.

Because sentinel values are specifically selected to be very differentfrom the real data values, they can introduce a very high discontinuityin regions where data values and null values touch each other. Thisdiscontinuity can cause problems with data compression algorithms,specifically those using signal processing techniques like DiscreteCosine Transform (DCT). DCT based techniques work well for data setsthat have subregions with very high grid cell similarity.

4.2. Efficiently Storing Null Values in Gridded Data

Embodiments of the present disclosure provide systems and methods thatefficiently store null values in gridded data while avoiding thenegative effects on data compression that using sentinel values causes.In an embodiment, null values are stored in a separate data structurefrom the data structure used to store the gridded data. For example, inan embodiment, null values are represented using a simple binary gridwhich only contains 0 or 1 values (e.g., 0 for null and 1 for realdata). In an embodiment, the grid can then be encoded (e.g., zig-zagencoded) to make it usable as a linear signal. Then, in an embodiment,the linear signal of 0's and 1's is run length encoded (RLE). FIG. 9 isa diagram illustrating how a 2 dimensional (2D) grid of data can bezig-zag encoded to create a linear signal in accordance with anembodiment of the present disclosure.

In an embodiment, gridded data can be represented by collections of many2D grids. In a numerical ocean model example, there might be model gridsfor 100 different water depths and over 40 different time steps,resulting in 4000 separate 2D grids. Locations of null values will oftenbe very similar between proximate 2D grids (e.g., up to 98% to 99% ofthe null values can be the same between proximate 2D grids).

In an embodiment comprising multiple 2D grids for a data set, only nullvalues for the first 2D grid are stored in a RLE sequence, and nullvalues from subsequent grids are separately stored (e.g., as (x, y)points). In an embodiment, only (x, y) indexes of grid cells with achange in null value from the previous grid are stored. This generates asequence of (x, y) index values. The RLE base grid and the sequence ofdifferent indexes can then be compressed (e.g., using a standardlossless compression algorithm such as ZIP, BZIP or deflate). A typicalcompression ratio is greater than 100 to 1.

Embodiments of the present disclosure can also replace the null markervalues in the original grid. Several options can be used to replace nullmarkers that will minimally disrupt transform based compression. Forexample, these options can include the local mean or median, the globalmean or median, dilated mean or median fill, and/or bi-linear orbi-cubic interpolated fill. For example, in a fill operation, a 3×3,4×4, 8×8, or 16×16 window can be moved over the grid, and null valuescan be replaced with the mean or median from that moving window.

In an embodiment, specific attributes of the data and performancerequirements dictate which replacement technique to use (e.g., areplacement technique with the best compression to loss performance canbe selected). In an embodiment, the data grid can be decompressed usingan appropriate technique, and then the decompressed null map can be usedto put the null values back into the data grid. In an embodiment, somecompression blocks are all null and can be skipped altogether andreconstructed from the null mask during decompression.

4.3. Exemplary Systems and Methods for Efficiently Storing Null Valuesin Gridded Data

FIG. 10 is a flowchart of an exemplary method for efficiently storingnull value in gridded data in accordance with an embodiment of thepresent disclosure. In step 1002, one or more data sets comprising nullvalues are received (e.g., by computing device 102). In step 1004, nullvalues for a first data set are stored in a separate data structure. Forexample, in an embodiment, computing device 102 (e.g., using datacompressor 104 and/or processor 106) can store null values for a firstdata set in the received data sets in a separate data structure, such asa binary grid. In an embodiment, the binary grid can be stored (e.g., inmemory 108).

In step 1006, a linear signal is generated based on the null values. Forexample, in an embodiment, computing device 102 (e.g., using datacompressor 104 and/or processor 106) encodes the binary grid (e.g.,using zig-zag encoding) into a linear signal. In an embodiment, thelinear signal can then be run length encoded. In step 1008, null valuesfor subsequent data sets in the one or more data sets are stored as asequence of (x, y) points. For example, in an embodiment, if thereceived data sets include a plurality of data sets, only null valuesfor the first data set can be stored as a separate data structure, andnull values for additional data sets can be stored using a sequence of(x, y) points. For example, computing device 102 (e.g., using datacompressor 104 and/or processor 106) can store (e.g., in memory 108)null values for each additional data set as a (x, y) index values in asequence of (x, y) points.

In step 1010, a null map is generated (e.g., using data compressor 104and/or processor 106) based on the linear signal and (if multiple datasets are present) the sequence of (x, y) points. In an embodiment, thelinear signal and the sequence of (x, y) points are compressed to formthe null map. For example, in an embodiment, computing device 102 (e.g.,using data compressor 104 and/or processor 106) can compress the linearsignal and the sequence of (x, y) points. In an embodiment, the null mapis formed using the uncompressed linear signal and (if multiple datasets are present) the sequence of (x, y) points.

In step 1012, null marker values in the one or more data sets arereplaced. For example, in an embodiment, computing device 102 (e.g.,using data compressor 104 and/or processor 106) can replace the nullmarker values in the original received one or more data sets (e.g.,using a fill operation with a moving window that replaces null valueswith a mean or median from values within the moving window).

Once the null values have been replaced using the method of FIG. 10 ,data in the original data sets can be more easily compressed (e.g., bycompressor 104) and transmitted (e.g., over a low bandwidthcommunication link). In an embodiment, when the compressed original oneor more data sets are transmitted, the null map can also be transmitted(e.g., to an end user such as end user device 114 a). In an embodiment,when the compressed original one or more data sets are received, theycan be decompressed using the null map. For example, in an embodiment,end user device 114 a can use the null map to decompress the originalone or more data sets (e.g., using data compressor 116 a). In anembodiment, the decompressed null map can be used to put the null valuesback into the decompressed original one or more data sets at locationsindicated by the null map.

4.4. Exemplary Advantages of Efficiently Storing Null Values in GriddedData

Embodiments of the present disclosure for efficiently storing nullvalues in gridded data have several advantages. For example, embodimentsof the present disclosure allow for null values to be compressedlosslessly, while the actual data grids are compressed in a lossymanner. This is important because scientific data grids made up offloating point values can be stored lossy with little or no impact onuse or interpretation of the data.

Embodiments of the present disclosure can be used to improve theperformance of all grid compression algorithms. Using null marker valuesmakes compression less efficient mostly due to signal processingproblems caused by discontinuities in the source data.

Embodiments of the present disclosure can be used to improve blockoriented compression techniques. As the grid data is divided into blocksfor compression, the null map can be queried and all null blocks cansimply be skipped altogether. On decompression, the null map can bereferenced and used to reconstruct the skipped blocks.

5. Eliminating Growth of Error in Compressed Time Series Data

Data sets of climatological measurements, such as data sets fornumerical ocean models, are typically generated based on data gatheredover multiple time steps from the same geographic area. For example,data sets for weather forecasting can typically include data gatheredevery 1 to 3 hours over a 3 to 7 day period. Climatological data setsmight include time steps over a many year period. Such data sets caninclude large amounts of data that can be difficult to work with unlesscompressed. This presents an opportunity for improved compressionperformance. Compression algorithms take advantage of statisticalsimilarities between segments of a data series to store the data seriesusing fewer bytes. Time sequenced areas of numerical environmentalmodels are highly correlated. The temperature of the ocean at time zerois a very good prediction of the temperature at time zero plus one hour.Embodiments of the present disclosure provide systems and methods forallowing time based series of gridded scientific data to be compressedmore efficiently.

5.1. Time Series Compression

FIG. 11A shows diagrams illustrating the creation of an exemplary timeseries difference grid in accordance with an embodiment of the presentdisclosure. FIG. 11A shows a first 4×4 grid of data 1102 at a first timestep T0 and a second 4×4 grid of data 1104 at a the next time step T1. Athird 4×4 grid of data 1106 shows the differences between each elementin grids 1102 and 1104 (i.e., T1P=T1−T0).

In FIG. 11A, the difference grid 1106 for T1P is much more compressiblethan grid 1104 for T1. With lossy compression and time differencing,signal drifting can occur over time. For example, if there is 0.1degrees of average loss at T0, this can accumulate and be ˜4 degrees byT40. With ocean models, time differences are much more correlated thandepth differences. Ocean models are closely spaced near the surface(e.g., 10 meters) but sparsely spaced towards the bottom (e.g., 500meters)

5.2. Eliminating Growth of Error in Compressed Time Series Data

Embodiments of the present disclosure use the natural correlation oftime sequenced model data to improve compression performance, especiallyfor lossy compression algorithms. To address issues with signal drift,embodiments of the present disclosure use the lossy de-compressed signalat each stage to compute the time based difference (e.g.,T1P=T1−T0_(Decompressed)). In an embodiment, the decompressed version ofT0 is reproduced exactly, and this eliminates any drift over time. Thisstep can require a large amount of computation, because each block hasto be compressed and then decompressed during initial compression.However, in an embodiment this step can be performed using asupercomputer (e.g., supercomputer 216) after model generation, whereresources are less of an issue. In an embodiment, decompressionperformance is not impacted by this step.

In an embodiment, for a sequence of gridded data sets, the first dataset can be labeled as T0, and subsequent data sets can be labeled as T1,T2, . . . , TN. In an embodiment, all data sets with time greater thanzero are replaced with the difference between the current data set andthe immediately previous set. So, in an embodiment the original data setfor T0 remains unchanged (e.g., as shown by grid 1102), but the data setfor T1 is replaced by the data set for T1P (e.g., grid 1104 is replacedwith grid 1106). Likewise, for example, a data set for T2 is replaced bya difference grid generated by subtracting (e.g., element by element)data in the data set at T1 from data in the data set at T2 (e.g.,T2P=T2−T1). Difference grids can be generated for data sets at each timesets, up to TN. In an embodiment, these difference grids are morecompact than the originals and are much more compressible.

In an embodiment, when the data is decompressed, this process can bereversed. For example, in an embodiment, the values for the data set1102 at T0 are added to values for data set 1106 (T1P) to get theoriginal values for the data set 1104 at T1 (e.g., with some lossaccounted for in a lossy compression algorithm). This process can createa problem for lossy compression algorithms. Since each step of thecompression is tied to the immediately preceding one, and each stepintroduces some loss, the loss can naturally accumulate as we proceed.For example, there might be 0.01 degree loss for T0, but 0.09 degreeloss for T6.

In an embodiment, to address this issue, when the data set 1102 for T0is compressed, it is immediately decompressed into another data set(e.g., T0E). In an embodiment, this data set contains the original datawith some error from the lossy compression. In an embodiment, the T0Edata set is what can be recovered via decompression. Then, to create thedata set 1106 at T1P for storage, instead of using the original data set1102 at T0, the data set at T0E can be used, so that the data set 1106at T1P is now T1P=T1−T0E.

FIG. 11B shows diagrams illustrating the creation of an exemplary timeseries difference grid accounting for compression error in accordancewith an embodiment of the present disclosure. In FIG. 11B, grid 1108 iscreated for T0E by compressing data from grid 1102 at time T0 (e.g.,using a compression algorithm) and then decompressing the compresseddata. As illustrated by FIG. 11B, there are some differences betweengrids 1102 and 1108 due to compression error. In FIG. 11B, to accountfor this compression error, the difference grid 1110 is calculated bysubtracting (e.g., element by element) data in grid 1108 from data ingrid 1104 (e.g., T1P=T1−T0E).

In an embodiment, this process can be followed for all subsequent timesteps. For example, a data set for T2 can be replaced by firstcompressing and decompressing data set T1 to generate T1E (which cancontain some compression error) and then generating a difference grid bysubtracting (e.g., element by element) data in T1 E from data in thedata set at T2 (e.g., T2P=T2−T1E). Difference grids can be generated fordata sets at each time sets, up to TN. By using this method, thecompression error does not accumulate over time.

5.3. Exemplary Systems and Methods for Eliminating Growth of Error inCompressed Time Series Data

FIG. 12 is a flowchart for a method for eliminating growth of error incompressed time series data in accordance with an embodiment of thepresent disclosure. In step 1202, time series data for N time steps isreceived. For example, in an embodiment, computing device 102 receivestime series data for N time steps. In step 1204, for each data set fromT0 to TN−1, an error grid is generated by compressing and decompressingdata in the data set. For example, in an embodiment, computing device102 (e.g., using data compressor 104 and/or processor 106) compressesdata in data sets for times T0, T1, . . . , TN−1 (e.g., using acompression algorithm) and then decompresses the compressed data at eachtime set, thereby generating error grids T0E, T1E, . . . , TN−1E.

In step 1206, for each data set from T1 to TN, a difference grid isgenerated by subtracting data in the error grid at the previous timestep from data in the data set at the current time step. For example, inan embodiment, computing device 102 (e.g., using data compressor 104and/or processor 106) generates difference grids for each time step bycalculating T1P=T1−T0E, T2P=T2−T1 E, . . . , TNP=TN−T(N−1)E. In step1208, data in the original data set at T0 is compressed, and data in thedifference grids generated for T1 to TN is compressed. For example, inan embodiment, data compressor 104 can compress data at the originaldata set at T0 and data in each generated difference grid (T1 P, T2P, .. . , TNP).

In an embodiment, when the compressed data at T0 and difference gridsare transmitted, the error grids can also be transmitted. For example,in an embodiment, computing device 102 can transmit the compressed dataat T0 and difference grids to end user device 114 a. In an embodiment,end user device 114 a can use the error grids to recreate the originaldata sets. For example, in an embodiment, end user device 114 a (e.g.,using data compressor 116 a and/or processor 118 a) can calculateT1=T1P+T0E, T2=T2P+T1E, . . . , TN=TNP+T(N−1)E.

5.4. Exemplary Variations for Eliminating Growth of Error in CompressedTime Series Data

In an embodiment, the T0 and TN grid sets can be used as bookendcontrols for the TX sets in between T0 and TN. In an embodiment, anynumber of intermediate TX sets can be used as support controls in theprocess to manage the error. In practice, the simple use of the T0 asthe first control presents the most effective and efficient way tocompensate for error.

5.5. Exemplary Advantages of Eliminating Growth of Error in CompressedTime Series Data

Embodiments of the present disclosure use highly correlated data in aknown configuration to improve compression, which has significantadvantages over prior techniques. Most compression algorithms depend onunknown statistical correlations to achieve compression. Embodiments ofthe present disclosure control not only the original data but the lossydata to eliminate signal drift over time, take advantage of timecorrelations to compress data highly, completely eliminates time baseddrift. And can enable tuning of compression to actual weather events.Embodiments of the present disclosure make traditional, straight-forwardDCT/QM/Entropy coding techniques practical for model data.

6. Exemplary Combination of Systems and Methods for Reducing Error inData Compression and Decompression

While embodiments of the present disclosure for variable block sizecompression for gridded data, efficiently storing null values in griddeddata, and eliminating growth of error in compressed time series datahave been described separately above (e.g., with reference to FIGS. 8,10, and 12 ), it should be understood that systems and methods forreducing error in data compression and decompression in accordance withembodiments of the present disclosure can use none, a plurality, or allof these techniques when compressing and/or decompressing data. Forexample, in an embodiment, all three of the methods shown in FIGS. 8,10, and 12 can be applied to a data set to be compressed (e.g., if thedata set includes null values and time series data). In an embodiment,two of the above methods shown in FIGS. 8, 10, and 12 can be applied toa data set to be compressed. For example, in an embodiment, if a dataset does not contain null values, the method of FIG. 10 is not applied,and if a data set does not contain time series data, the method of FIG.12 is not applied.

In an embodiment, a data compressor (e.g., data compressor 104) candetermine which, if any, of the methods of FIGS. 8, 10, and 12 to apply.FIG. 13 is a flowchart of a method for determining how to reducing errorin data compression in accordance with an embodiment of the presentdisclosure. In step 1202, data to be compressed is received. Forexample, in an embodiment, supercomputer 216 receives the data to becompressed (e.g., from satellite 204). In step 1204, if the data setincludes time series data, the data is compressed using determineddifference grids and error grids. For example, in an embodiment,supercomputer 216 (e.g., using data compressor 218 and/or processor 220)determines whether the data set includes time series data and, if so,compresses the data using the method of FIG. 12 . In step 1206, if thedata set includes null values, the data is compressed by generating anull map. For example, in an embodiment, supercomputer 216 (e.g., usingdata compressor 218 and/or processor 220) determines whether the dataset includes null values, and if so, compresses the data using themethod of FIG. 10 . In step 1208, if the data set does not include nullvalues or time series data, the data is compressed using variable blocksize compression. For example, in an embodiment, if supercomputer 216(e.g., using data compressor 218 and/or processor 220) determines thatthe data set does not include time series data or null values,supercomputer 216 (e.g., using data compressor 218 and/or processor 220)compresses the data using the method of FIG. 8 .

In an embodiment, similar methods can be used to decompress the data.For example, if ship 226 a receives data to be decompressed fromsupercomputer 216, ship 226 a can determine how to decompress the databased on the type of compressed data received. For example, if ship 226a receives an error grid with the compressed data, ship 226 a candetermine (e.g., using processor 230 a and/or data compressor 228 a)that the compressed data included time series data and can use the errorgrid to decompress the data as described above. In an embodiment, ifship 226 a receives a null map with the compressed data, ship 226 a candetermine (e.g., using processor 230 a and/or data compressor 228 a)that the compressed data included null values and can use the null mapto decompress the data as described above. Additionally, in anembodiment, a sending device (e.g., supercomputer 216) can be configuredto send information (e.g., in a header) to a receiving device (e.g.,ship 226 a) instructing the receiving device how to decompress thecompressed data.

7. Conclusion

It is to be appreciated that the Detailed Description, and not theAbstract, is intended to be used to interpret the claims. The Abstractmay set forth one or more but not all exemplary embodiments of thepresent disclosure as contemplated by the inventor(s), and thus, is notintended to limit the present disclosure and the appended claims in anyway.

The present disclosure has been described above with the aid offunctional building blocks illustrating the implementation of specifiedfunctions and relationships thereof. The boundaries of these functionalbuilding blocks have been arbitrarily defined herein for the convenienceof the description. Alternate boundaries can be defined so long as thespecified functions and relationships thereof are appropriatelyperformed.

The foregoing description of the specific embodiments will so fullyreveal the general nature of the disclosure that others can, by applyingknowledge within the skill of the art, readily modify and/or adapt forvarious applications such specific embodiments, without undueexperimentation, without departing from the general concept of thepresent disclosure. Therefore, such adaptations and modifications areintended to be within the meaning and range of equivalents of thedisclosed embodiments, based on the teaching and guidance presentedherein. It is to be understood that the phraseology or terminologyherein is for the purpose of description and not of limitation, suchthat the terminology or phraseology of the present specification is tobe interpreted by the skilled artisan in light of the teachings andguidance.

Any representative signal processing functions described herein can beimplemented using computer processors, computer logic, applicationspecific integrated circuits (ASIC), digital signal processors, etc., aswill be understood by those skilled in the art based on the discussiongiven herein. Accordingly, any processor that performs the signalprocessing functions described herein is within the scope and spirit ofthe present disclosure.

The above systems and methods may be implemented as a computer programexecuting on a machine, as a computer program product, or as a tangibleand/or non-transitory computer-readable medium having storedinstructions. For example, the functions described herein could beembodied by computer program instructions that are executed by acomputer processor or any one of the hardware devices listed above. Thecomputer program instructions cause the processor to perform the signalprocessing functions described herein. The computer program instructions(e.g., software) can be stored in a tangible non-transitory computerusable medium, computer program medium, or any storage medium that canbe accessed by a computer or processor. Such media include a memorydevice such as a RAM or ROM, or other type of computer storage mediumsuch as a computer disk or CD ROM. Accordingly, any tangiblenon-transitory computer storage medium having computer program code thatcause a processor to perform the signal processing functions describedherein are within the scope and spirit of the present disclosure.

While various embodiments of the present disclosure have been describedabove, it should be understood that they have been presented by way ofexample only, and not limitation. It will be apparent to persons skilledin the relevant art that various changes in form and detail can be madetherein without departing from the spirit and scope of the disclosure.Thus, the breadth and scope of the present disclosure should not belimited by any of the above-described exemplary embodiments.

What is claimed is:
 1. A method, comprising: receiving, by a datacompressor device, data to be compressed; determining, by the datacompressor device, whether the data includes time series data or nullvalues; responsive to determining that the data includes time seriesdata, generating, by the data compressor device, a plurality of errorgrids and difference grids for a plurality of data sets at acorresponding plurality of time steps and compressing the data based onthe error grids and the difference grids; responsive to determining thatthe data includes null values, generating, by the data compressordevice, a null map based on a linear signal associated with the nullvalues and compressing the data based on the null map; and responsive todetermining that the data excludes time series data or null values,compressing, by the data compressor device, the data using variableblock size compression based on a comparison of decompressed data tooriginal data.
 2. The method of claim 1, wherein the plurality of timesteps comprise time steps from a first time step T0 in the plurality oftime steps to a last time step TN in the plurality of time steps.
 3. Themethod of claim 2, wherein the plurality of error grids correspond toeach data set in the plurality of data sets at time steps T0 to T(N−1).4. The method of claim 3, wherein the plurality of difference gridscorrespond to each data set in the plurality of data sets at time stepsT1 to TN, and wherein generating the plurality of difference gridsfurther comprises for each data set in the plurality of data sets attime steps T1 to TN, generating a corresponding difference grid bysubtracting data in a corresponding error grid in the plurality of errorgrids at a previous time step from data in a corresponding data set inthe plurality of data sets at a current time step.
 5. The method ofclaim 1, further comprising replacing the null values in the data with amean or median of values in a window of values comprising the nullvalues and neighboring non-null values.
 6. The method of claim 1,wherein the data comprises a plurality of data sets, and whereingenerating the linear signal further comprises generating a linearsignal for a first data set in the plurality of data sets based on nullvalues in the first data set.
 7. The method of claim 1, whereincompressing the data using variable block size compression based on acomparison of decompressed data to original data comprises: compressingthe data into compressed data; decompressing the compressed data intodecompressed data; and subdividing the data in response to adetermination that a difference between the data and the decompresseddata exceeds a predetermined threshold.
 8. The method of claim 7,further comprising determining that a compression error has occurred forthe data in response to the determination that the difference betweenthe data and the decompressed data exceeds the predetermined threshold.9. The method of claim 8, further comprising: determining that nocompression error has occurred for the data in response to thedetermination that the difference between the data and the decompresseddata did not exceed the predetermined threshold; and transmitting thecompressed data in response to determining that no compression error hasoccurred for the data.
 10. The method of claim 1, further comprisingtransmitting the generated compressed data to an end user device. 11.The method of claim 10, wherein the compressed data is transmitted overa low bandwidth communication link.
 12. A device, comprising: aprocessor; a memory; and a data compressor device, coupled to theprocessor and memory, configured to: receive data to be compressed;determine whether the data includes time series data or null values;responsive to determining that the data includes time series data,generate a plurality of error grids and difference grids for a pluralityof data sets at a corresponding plurality of time steps and compress thedata based on the error grids and the difference grids; responsive todetermining that the data includes null values, generate a null mapbased on a linear signal associated with the null values and compressthe data based on the null map; and responsive to determining that thedata excludes time series data or null values, compress, by the datacompressor device, the data using variable block size compression basedon a comparison of decompressed data to original data.
 13. The device ofclaim 12, wherein the plurality of time steps comprise time steps from afirst time step T0 in the plurality of time steps to a last time step TNin the plurality of time steps.
 14. The device of claim 13, wherein theplurality of error grids correspond to each data set in the plurality ofdata sets at time steps T0 to T(N−1).
 15. The device of claim 14,wherein the plurality of difference grids correspond to each data set inthe plurality of data sets at time steps T1 to TN, and whereingenerating the plurality of difference grids further comprises for eachdata set in the plurality of data sets at time steps T1 to TN,generating a corresponding difference grid by subtracting data in acorresponding error grid in the plurality of error grids at a previoustime step from data in a corresponding data set in the plurality of datasets at a current time step.
 16. The device of claim 12, wherein thedata compressor device is further configured to replace the null valuesin the data with a mean or median of values in a window of valuescomprising the null values and neighboring non-null values.
 17. Thedevice of claim 12, wherein the data comprises a plurality of data sets,and wherein generating the linear signal further comprises generating alinear signal for a first data set in the plurality of data sets basedon null values in the first data set.
 18. The device of claim 12,wherein compressing the data using variable block size compression basedon a comparison of decompressed data to original data comprises:compressing the data into compressed data; decompressing the compresseddata into decompressed data; and subdividing the data in response to adetermination that a difference between the data and the decompresseddata exceeds a predetermined threshold.
 19. The device of claim 18,wherein the data compressor device is further configured to determinethat a compression error has occurred for the data in response to thedetermination that the difference between the data and the decompresseddata exceeds the predetermined threshold.
 20. The device of claim 19,wherein the data compressor device is further configured to: determinethat no compression error has occurred for the data in response to thedetermination that the difference between the data and the decompresseddata did not exceed the predetermined threshold; and transmit thecompressed data in response to determining that no compression error hasoccurred for the data.
 21. The device of claim 12, wherein the datacompressor device is further configured to transmit the generatedcompressed data to an end user device.
 22. The device of claim 21,wherein the generated compressed data is transmitted over a lowbandwidth communication link.